MicroRNA-Dependent Mechanisms Underlying the Function of a β-Amino Carbonyl Compound in Glioblastoma Cells

Glioblastoma (GB) is an aggressive brain malignancy characterized by its invasive nature. Current treatment has limited effectiveness, resulting in poor patients’ prognoses. β-Amino carbonyl (β-AC) compounds have gained attention due to their potential anticancerous properties. In vitro assays were performed to evaluate the effects of an in-house synthesized β-AC compound, named SHG-8, upon GB cells. Small RNA sequencing (sRNA-seq) and biocomputational analyses investigated the effects of SHG-8 upon the miRNome and its bioavailability within the human body. SHG-8 exhibited significant cytotoxicity and inhibition of cell migration and proliferation in U87MG and U251MG GB cells. GB cells treated with the compound released significant amounts of reactive oxygen species (ROS). Annexin V and acridine orange/ethidium bromide staining also demonstrated that the compound led to apoptosis. sRNA-seq revealed a shift in microRNA (miRNA) expression profiles upon SHG-8 treatment and significant upregulation of miR-3648 and downregulation of miR-7973. Real-time polymerase chain reaction (RT-qPCR) demonstrated a significant downregulation of CORO1C, an oncogene and a player in the Wnt/β-catenin pathway. In silico analysis indicated SHG-8’s potential to cross the blood–brain barrier. We concluded that SHG-8’s inhibitory effects on GB cells may involve the deregulation of various miRNAs and the inhibition of CORO1C.


SUPPLEMENTARY METHODOLOGY SM1. Synthesis of SHG-8
The sustainable sulfonic acid-functionalized silica nanospheres (SAFSNS) nano-catalyst was prepared and characterized according to Ahmad et al. (2021) (1).The desired compound SHG-8 was synthesized by the SAFSNS (0.04g) catalysed Mannich reaction of acetophenone (1.1mmol), 4-Bromo benzaldehyde (1mmol), and aniline (1mmol) in one pot synthesis by our reported procedure (2).The reaction mixture in ethanol at ambient temperature was stirred for four hours.The reaction progress was monitored by the TLC, and the solvent was evaporated to get a yellow solid product on completion of the reaction.The catalyst SAFSNS was separated from the solution of crude product in dichloromethane at 35°C by filtration.Subsequently, the reaction product was recrystallized in ethanol solvent as a light yellow solid and well characterised by the IR, 1 H-NMR, 13 C-NMR, and HRMS techniques.

SM2. MTT cell viability assay
Cell viability of U87MG and U251MG GB cells was performed as previously described by Vazhappilly el al.

SM3. Colony forming assay
Colony formation potential of U87MG and U251MG cells was assessed as described by Vazhappilly et al.
(2021) (3).Cells at a density of 500 cells/well were seeded in triplicates on 12-well plates and incubated with SHG-8 (50μΜ and 100μΜ) or <1% DMSO (negative control) or 200μΜ cis-platin (positive control), for 24 hours in complete media.Drug-supplemented media was removed the next day, and the plates were further incubated for five days at 5% CO2 and 37 o C in a humidifying incubator.Formed colonies were fixed with 4% paraformaldehyde (PFA) (Sigma-AldrichTM, Dorset, UK) for 25min, followed by staining with 0.1% crystal violet (Sigma-AldrichTM, Dorset, UK).The stained colonies were washed with distilled water and left to air dry overnight.Colonies containing more than 30 cells were microscopically photographed and quantitatively assessed under a light microscope (Olympus Life Science Solutions, Stansted, UK).

SM4. Scratch assay
A wound healing assay was performed as previously described by Vazhappilly et al. (2021) (3).U87MG and U251MG cells were seeded in a 12-well plate at a cell density of 2.5x10 5 cells/well.Scratches were performed as straight lines across the wells with a 2μL pipette tip before the addition of the treatment conditions, and the wells were washed twice with PBS to remove any cell debris lifted while performing the scratches.U87MG cells were treated with <1% DMSO (negative control) or 200μΜ cis-platin (positive control), and two concentrations of SHG-8 (20μΜ and 40μΜ).U251MG cells were treated with <1% DMSO (negative control) or 200μΜ cis-platin (positive control) and two concentrations of SHG-8 (50μΜ and 100μΜ).Images of the scratch midlines were taken at 0h, 24h, and 48h using a light microscope.The migration ability of the cells was analysed via using the ImageJ software with the assistance of a wound healing plugin (4,5).The percentage of the healed area of the scratch at 24 and 48 hours was normalized to t0 (0 hours) for all treatments to facilitate cross-comparisons.

SM5. RNA isolation and TaqMan expression assays
RT-qPCR was performed as described by Braoudaki et al. (2016) (6).In brief, total RNA and miRNAs were extracted following the Trizol reagent (Ambion Life Technology, Aukland, New Zealand) protocol and mirVana isolation kit (ThermoFisher, Vilnius, Lithuania), respectively.The sample's quantity and quality were assessed using Nanodrop (Nanodrop ND1000 Spectrophotometer, (Marshall Scientific, Hampton, USA).cDNA synthesis reactions were performed by using a Thermal cycler (Eppendorf, Mastercycler nexus gradient, ThermofisherTM, CA, USA) by using High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems ThermoFisher, Pleasanton, CA).RT-qPCR experiments were performed by using QuantStudio™ Real-Time PCR (Quant Studio 7 flex, Applied Biosystems, Massachusetts, USA).For mRNA profiling, the thermal cycler ran at 25°C for 10min, followed by 37°C for 120min, and 85°C for 5min, whereas the PCR cycles ran at 95°C for 20sec, followed by 40 cycles of 95°C for 1sec and 60°C for 20sec.For miRNA profiling, the thermal cycler ran at 16°C for 30min, followed by 42°C for 30min, and 85°C for 5min, whereas the PCR cycles ran at 95°C for 10min, followed by 40 cycles of 95°C for 15sec and 60°C for 1min.Expression analysis in different samples was performed by using specific primers for each gene and miRNAs. 2 -∆∆Ct values of fold expression were used to compare the relative differences between the SHG-8 treated samples and the DMSO control samples.

SM6. Library preparation for sRNA sequencing
A total of 1.5μg RNA per sample was used as input material for the RNA sample preparations.Sequencing libraries were generated using NEBNext®Ultra™ small RNA Sample Library Prep Kit for Illumina (NEB, USA) following the manufacturer's recommendations, and index codes were added to attribute sequences to each sample.Firstly, ligated the 3′ SR Adaptor was made via mixing 3′ SR Adaptor for Illumina, RNA and Nuclease-Free Water.The mixture system was incubated for 2min at 70°C in a preheated thermal cycler.The tube was transferred on ice.Then, 3′ Ligation Reaction Buffer (2X) and 3′ Ligation Enzyme Mix ligate the 3′ SR Adaptor were added, and the mixture was incubated for 1 hour at 25°C in a thermal cycler.To prevent adaptor-dimer formation, the SR RT Primer hybridizes to the excess of 3′ SR Adaptor (that remains free after the 3′ ligation reaction) and transforms the single-stranded DNA adaptor into a double-stranded DNA molecule.Secondly, ligation of the 5′ SR Adaptor was performed, followed by reverse transcription synthetic chain reaction.Lastly, PCR amplification and Size Selection followed.PAGE gel was used for electrophoresis fragment screening purposes and rubber cutting recycling as the pieces get small RNA libraries.PCR products were purified (AMPure XP system), and library quality was assessed on the Agilent Bioanalyzer 2100 system.

SM7. Clustering and sequencing
The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v4-cBot-HS (Illumia) according to the manufacturer's instructions.After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2500 platform and paired-end reads were generated.

SM8. Data analysis
Raw data (raw reads) of fastq format were first processed through in-house perl scripts.In this step, clean data (clean reads) were obtained by removing reads containing adapter, ploy-N and low-quality reads from raw data, and reads were trimmed and cleaned by removing the sequences smaller than 18 nucleotides or longer than 30 nucleotides.At the same time, Q20, Q30, GC-content and sequence duplication level of the clean data were calculated.All the downstream analyses were based on clean data with high quality.

SM9. Comparative analysis
Utilisation of the Bowtie tools software, in particular The Clean Reads with Silva database, GtRNAdb database, Rfam database, and Repbase database sequence alignment were incorporated in filtering ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA) and other ncRNA and repeats.The remaining reads were used to detect known miRNA and new miRNA predicted by comparing with known miRNAs from miRBase.The Randfold tools were used for the prediction of new miRNA secondary structure.

SM11. Quantification of miRNA expression levels
miRNA expression levels were estimated for each sample: 1. sRNAs were mapped back onto the precursor sequence.2. The read count for each miRNA was obtained from the mapping results.

SM12. Differential expression analysis
For the samples with biological replicates: Differential expression analysis of two conditions/groups was performed using the DESeq R package (1.10.1).DESeq provides statistical routines for determining differential expression in digital miRNA expression data using a model based on the negative binomial distribution.The resulting p values were adjusted using Benjamini and Hochberg's approach for controlling the false discovery rate.miRNA with an adjusted p<0.05 found by DESeq were assigned as differentially expressed.For the samples without biological replicates: Prior to differential gene expression analysis, for each sequenced library, differential expression analysis of two samples was performed using the IDEG6.The p value was adjusted using q value (7).Q value<0.005& |log2 (foldchange) |≥1 was set as the threshold for significantly differential expression.

SM13. GO enrichment analysis and KEGG pathway enrichment analysis
Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was implemented by the GOseq R packages based on Wallenius non-central hyper-geometric distribution.KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism, and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/)(8).We used KOBAS software to test the statistical enrichment of differential expression genes in KEGG pathways (9).